Post-Pandemic Recruitment Methods for Conducting School-Based Research
Bibliographic record
Abstract
Abstract School-based research is valuable for understanding and improving educational practices and outcomes, but study recruitment in school settings can often be challenging, particularly after the COVID-19 pandemic. As school-based recruitment efforts have increasingly shifted online, researchers must consider and employ effective strategies when recruiting participants using digital communication tools like email. This short report reflects on anecdotal experiences from two studies conducted in elementary schools in the United States (US) and Canada to provide an overview of different practical techniques researchers can use to design email recruitment plans for school-based research. Notably, researchers may benefit from using web-based tools to create comprehensive and representative recruitment lists. Emails that feature concise and personalized messages with videos or graphics may cater to educators' needs and priorities. Strategically timing recruitment and reminder emails to match school calendars and educators’ schedules may help to align recruitment with the school calendar. Limitations related to the restricted generalizability of the sample and the need for further empirical research to test these methods are discussed. Future research should explore methods for recruiting other important school stakeholders (e.g., caregivers and students) and other recruitment tools (e.g., social media and video software).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.378 | 0.428 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".